Use getAggregatedTableData function to download the data aggregated at a varying range of time.
Specify following parameters to retrieve the data from CMAP.
Call plot_ts function to obtain plot_ly/ggplot object.
con <- dbConnect(odbc::odbc(), DSN="CMAP-MSSQL",UID="ArmLab",PWD="ArmLab2018")
# Input variable:
table.list <- c('tblSST_AVHRR_OI_NRT', 'tblAltimetry_REP', 'tblPisces_NRT')
var.list <- c('sst', 'sla', 'NO3')
selIndex <- 1 # selected "sst" from the table "tblSST_AVHRR_OI_NRT"
table.name <- table.list[selIndex]
sel.var <- var.list[selIndex]
## Example I:
range.var <- list()
range.var$lat <- c(25,30)
range.var$lon <- c(-160,-155)
range.var$time <- c('2016-03-29', '2016-05-29')
## Subset selection: data retrieval
agg.var <- 'time'
tbl.subset <- getAggregatedTableData(con, table.name, sel.var, range.var, agg.var)
head(tbl.subset,20)
## # A tibble: 20 x 2
## time sst
## <date> <dbl>
## 1 2016-03-29 21.3
## 2 2016-03-30 20.6
## 3 2016-03-31 20.6
## 4 2016-04-01 20.7
## 5 2016-04-02 20.8
## 6 2016-04-03 20.9
## 7 2016-04-04 21.1
## 8 2016-04-05 20.9
## 9 2016-04-06 20.9
## 10 2016-04-07 20.7
## 11 2016-04-08 20.9
## 12 2016-04-09 21.0
## 13 2016-04-10 21.2
## 14 2016-04-11 21.1
## 15 2016-04-12 21.1
## 16 2016-04-13 21.1
## 17 2016-04-14 21.2
## 18 2016-04-15 21.4
## 19 2016-04-16 21.4
## 20 2016-04-17 21.4
## Plot -- Time series:
p <- plot_ts(tbl.subset,'plotly',sel.var)
p
dbDisconnect(con)
con <- dbConnect(odbc::odbc(), DSN="CMAP-MSSQL",UID="ArmLab",PWD="ArmLab2018")
selIndex <- 2 # selected "sla" from the table "tblAltimetry_REP"
table.name <- table.list[selIndex]
sel.var <- var.list[selIndex]
## Subset selection: data retrieval
agg.var <- 'time'
tbl.subset <- getAggregatedTableData(con, table.name, sel.var, range.var, agg.var)
head(tbl.subset,20)
## # A tibble: 20 x 2
## time sla
## <date> <dbl>
## 1 2016-03-29 0.0242
## 2 2016-03-30 0.0228
## 3 2016-03-31 0.0215
## 4 2016-04-01 0.0208
## 5 2016-04-02 0.0197
## 6 2016-04-03 0.0189
## 7 2016-04-04 0.0180
## 8 2016-04-05 0.0173
## 9 2016-04-06 0.0167
## 10 2016-04-07 0.0161
## 11 2016-04-08 0.0155
## 12 2016-04-09 0.0149
## 13 2016-04-10 0.0141
## 14 2016-04-11 0.0135
## 15 2016-04-12 0.0131
## 16 2016-04-13 0.0128
## 17 2016-04-14 0.0128
## 18 2016-04-15 0.0125
## 19 2016-04-16 0.0122
## 20 2016-04-17 0.0122
## Plot -- Time series:
p <- plot_ts(tbl.subset,'plotly',sel.var)
p
dbDisconnect(con)